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AI Machine Learning & Data Science Research

DeepMind’s Collect & Infer: A Fresh Look at Data-Efficient Reinforcement Learning

A DeepMind research team proposes Collect and Infer, a novel paradigm that explicitly models Reinforcement Learning (RL) as data collection and knowledge inference to dramatically boost RL data efficiency.

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Apple Neural TTS System Study: Combining Speakers of Multiple Languages to Improve Synthetic Voice Quality

An Apple research team explores multiple architectures and training procedures to develop a novel multi-speaker and multi-lingual neural TTS system. The study combines speech from 30 speakers from 15 locales in 8 languages, and demonstrates that for the vast majority of voices, such multi-lingual and multi-speaker models can yield better quality than single speaker models.

AI Machine Learning & Data Science Research

100+ Stanford Researchers Publish 200+ Page Paper on the AI Paradigm Shift Introduced by Large-Scale Models

In a 200+ page paper, Percy Liang, Fei-Fei Li, and over 100 other researchers from the Stanford University Center for Research on Foundation Models (CRFM) systematically describe the opportunities and risks of large-scale pretrained “foundation” models. The unique study aims to provide a clearer understanding of how these models work, when and how they fail, and the various capabilities provided by their emergent properties.

AI Machine Learning & Data Science Research

Logic Explained Deep Neural Networks: A General Approach to Explainable AI

A research team from Università di Firenze, Università di Siena, University of Cambridge and Universitè Côte d’Azur proposes a general approach to explainable artificial intelligence (XAI) in neural architectures, designing interpretable deep learning models called Logic Explained Networks (LENs). The novel approach yields better performance than established white-box models while providing more compact and meaningful explanations.

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Google Researchers Enable Transformers to Solve Compositional NLP Tasks

A Google Research team explores the design space of Transformer models in an effort to enable deep learning architectures to solve compositional tasks. The proposed approach provides models with inductive biases via design decisions that significantly impact compositional generalization, and achieves state-of-the-art results on the COGS and PCFG composition benchmarks.

AI Machine Learning & Data Science Research

DeepMind’s Perceiver IO: A General Architecture for a Wide Variety of Inputs & Outputs

A DeepMind research team proposes Perceiver IO, a single network that can easily integrate and transform arbitrary information for arbitrary tasks while scaling linearly with both input and output sizes. The general architecture achieves outstanding results on tasks with highly structured output spaces, such as natural language and visual understanding.

AI Machine Learning & Data Science Nature Language Tech Research

Google’s H-Transformer-1D: Fast One-Dimensional Hierarchical Attention With Linear Complexity for Long Sequence Processing

A Google Research team draws inspiration from two numerical analysis methods — Hierarchical Matrix (H-Matrix) and Multigrid — to address the quadratic complexity problem of attention mechanisms in transformer architectures, proposing a hierarchical attention scheme that has linear complexity in run time and memory.

AI Machine Learning & Data Science Nature Language Tech Research

Melbourne U, Facebook & Twitter Expose Novel Numerical Errors in NMT Systems

A research team from the University of Melbourne, Facebook AI, and Twitter Cortex proposes a black-box test method for assessing and debugging the numerical translation of neural machine translation systems in a systematic manner. The approach reveals novel types of errors that are general across multiple state-of-the-art translation systems.

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Quanergy’s 3D LiDAR Selected for First V2X Smart City Deployment in South Korea

On July 20, Quanergy Systems announced its 3D LiDAR solution has been selected to support the development of an Information, Communication, and Technology (ICT) system in Busan, South Korea. The ICT system is a key component of the South Korean government’s strategy to build data driven IoT smart cities. Busan is one of the pilot cities for the initiative.